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  3. Simulating Quadrupolar Nmr Dynamics In Solid Electrolyte Li10gep2s12.
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  3. Simulating Quadrupolar Nmr Dynamics In Solid Electrolyte Li10gep2s12.

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Simulating quadrupolar NMR dynamics in solid electrolyte Li10GeP2S12.

Tabea Huss1, Federico Civaia1, Simone S Köcher1,2

  • 1Fritz-Haber Institute of the Max Planck Society, Berlin (DE), Germany.

The Journal of Chemical Physics
|February 24, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning accelerates molecular dynamics simulations for solid electrolytes like lithium 10 germanium phosphorus sulfide (LGPS). This approach accurately predicts lithium ion diffusion dynamics, improving battery material analysis.

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Area of Science:

  • Solid-state chemistry
  • Materials science
  • Computational chemistry

Background:

  • Quadrupolar solid-state nuclear magnetic resonance (NMR) spectroscopy is sensitive to lithium ion diffusion dynamics in solid electrolytes.
  • Interpreting NMR data is challenging due to material complexity and motional narrowing effects.
  • Atomic simulations are computationally expensive and often use idealized models.

Purpose of the Study:

  • To develop a machine learning (ML)-assisted workflow to address experimental complexity in solid-state NMR studies of battery materials.
  • To enable microsecond-scale molecular dynamics (MD) simulations and predict electric field gradient (EFG) tensors for lithium ion conductors.
  • To accurately predict NMR observables and differentiate ion dynamics in lithium-ion battery materials.

Main Methods:

  • Utilized ML acceleration for microsecond-scale MD simulations of Li10GeP2S12 (LGPS).
  • Employed a tensorial model for efficient EFG tensor predictions from MD trajectories.
  • Computed temperature-dependent 7Li NMR quadrupolar observables and emulated spin-alignment echo (SAE) experiments in silico.

Main Results:

  • Achieved excellent agreement between predicted (24 kHz) and experimental (23 kHz) quadrupolar coupling for tetragonal LGPS.
  • Successfully predicted temperature dependence of 7Li NMR quadrupolar observables, accounting for motional narrowing.
  • Extracted correlation times for Li ion motion in different LGPS crystal structures using inverse Laplace transform on simulated SAE data.

Conclusions:

  • The ML-assisted workflow effectively overcomes limitations in interpreting complex solid-state NMR data for battery materials.
  • Accurate prediction of NMR observables and ion dynamics is achievable, enhancing understanding of lithium-ion diffusion.
  • The developed methodology shows promise for differentiating inter-grain vs. intra-grain ion dynamics in solid electrolytes.